Field of the Invention
The various embodiments relate generally to analyzing loudspeaker systems and, more specifically, to neural network-based parameter estimation for a lumped parameter model of a loudspeaker.
Description of the Related Art
Modeling the behavior of loudspeakers is a common step when designing and manufacturing an audio system. For example, a manufacturer may collect data for a loudspeaker once it reaches the end of a factory line in order to model the specific behavior and characteristics of the loudspeaker. The resulting model may then be implemented in a control system that corrects for linear and/or non-linear distortion of the loudspeaker, enabling a desired loudspeaker response to be achieved.
One well-known type of model that is oftentimes employed in loudspeaker control systems is the lumped parameter model. In general, the lumped parameter model of a loudspeaker includes values for a set of parameters that, together, approximate the behavior of the loudspeaker. The parameters and corresponding values used in the lumped parameter model reflect simplifying assumptions, such as a piston-like motion of the loudspeaker diaphragm, that enable simplified mathematical modeling of the components within the loudspeaker and, consequently, more efficient simulation of the loudspeaker.
In general, generating a lumped parameter model based on loudspeaker data collected at the end of a factory line enables the loudspeaker control system to accurately achieve a desired loudspeaker response. However, the accuracy of a lumped parameter model generated for a given loudspeaker can decrease over the useful life of the loudspeaker due to various physical changes that affect the loudspeaker components over time as a result of heating effects, wear and tear, etc. For example, materials used in the suspension of a loudspeaker can deteriorate with time and/or the electrical characteristics of a loudspeaker voice coil can change, causing the response of the loudspeaker to an input signal to differ substantially from the response predicted by a lumped parameter model for that input signal. In such a situation, the values assigned to parameters included in the lumped parameter model of the speaker no longer accurately reflect the mechanical and electrical characteristics of the loudspeaker; therefore, the lumped parameter model is unable to accurately capture the actual behavior of the loudspeaker.
As the foregoing illustrates, more effective techniques for estimating, over time, parameter values for lumped parameter models of loudspeakers would be useful.